1. From individuals to populations The basic entities of ecological research Modular organized Brown fungi Clonal Populus tremuloides forests Unitary organisms have genetically prescribed longivity Single celled Bacteria A modular organism has an indeterminate structure wherein modules of various complexity (e.g., leaves, twigs) may be assembled without strict limits on their number or placement. A clonal colony or genet is a group of genetically identical individuals, such as plants, fungi, or bacteria, that have grown in a given location, all originating vegetative, not sexually, from a single ancestor. In plants, an individual in such a population is referred to as a ramet. Bees, ants, and other insect societies form superorganisms that behave as an ecological unit. Single information coding strand of DNA Clonal organisms might have extreme longivity

2. Life cycles All organisms have life cycles from single celled zygotes through ontogenetic stages to adult forms. All organsims finally die. Mortality k1 k2 k3 k4 k5=1 Surviving individuals Individual age Type I Type II Type III Type I, high survivorship of young individuals: Large mammals, birds Type II, survivorship independent of age, seed banks Type III, low survivorship of young individuals, fish, many insects Surviving individuals Individual age Age dependent survival in annual plants Often stages of dormancy

3. K-factor analysis k1 k2 k3 k4 k5 Each life stage t has a certain mortality rate d t. Stage Number surviving Number of deaths Mortality rate K- factor Year2000 Number of eggs20003500 Stage 180012000.60k10.92 Stage 21206800.85k21.90 Stage 330900.75k31.39 Stage 45250.83k41.79 Stage 5140.80k51.61 k-factor 200120022003200420052006200720082009 33001500130015001000120010509801100 0.130.320.050.120.02 0.080.110.16 1.492.832.502.102.001.101.972.251.78 2.500.612.740.881.831.191.201.041.21 1.370.131.451.080.771.470.260.060.05 2.123.710.863.422.983.834.094.144.40 The k-factor is the difference of the logarithms of the number of surviving indiiduals at the beginning and the end of each stage. k-factors calculated for a number of yearsA simple life table

4. Time series Density series 2000 2001 No density dependence in mortality rates Clear temporal trends in mortality rates Density

5. Birds Various vertebrates Allometric constraints on life history parameters Mammals Insects Microorganisms

6. Trade-offs: Organisms allocate limited energy or resources to one structure or function at the expense of another. All species face trade-off. Trade-offs shape and constrain life history evolution. Number of offspring Survival probability Fitness Optimal offspring number Life history trade-offs Complex life histories appear to be one way to maximize reproductive success in such highly competitive environments. Time Degree of starvation Quality of food Optimal food intake time

7. The importance of individualistic behaviour X Amount of food consumed Food quality Food value The value of food is the product of food quality and the difference of total amount N and amount consumed C). Th perceived food value migh remain more stable than food quality For different individuals it pays to use resources of different quality. Trade-offs between resource quality and resource availability at a given point of time mark the beginn of individualistic behaviour. Individualistic behaviour is already observable in bacteria. The precise estimation of resource value is one of the motors of brain evolution.

8. Trade-off decisions during life history How long to live? How often to breed? (semelparous, iteroparous) Caring for offspring? When to begin reproducing? How many offspring? How fast to develop? When to change morphology? How fast to grow? How large to grow? What size of offspring? At each time step in life animals take decisions. These decisions determine future reproductive success and ae objects of selective forces Different selective forces might act on different stages of life. Contrary forces might cause the development of subpopulations. Each step is a decision on resource allocation. How long to live after reproduction?

9. Contrasting selective forces on life history Brookesia desperata Rana temporaria High reproduction rate High population growth Low parental investment No care of offspring Often unstable habitats Low reproduction rate Low population growth High parental investment Intensive care of offspring Often stable habitats r-selection and K-selection describe two ends of a continuum of reproductive patterns. Continuum K selected mature more slowly and have a later age of first reproduction have a longer lifespan have few offspring at a time and are iteroparous have a low mortality rate and a high offspring survival rate have high parental investment Have often relatively stable populations r selected species mature rapidly and have an early age of first reproduction have a relatively short lifespan have few reproductive events, or are semelparous have a high mortality rate and a low offspring survival rate have minimal parental care/investment are often highly variable in population size r refers to the high reproductive rate. K refers to the carrying capacity of the habitat In many species different developmental stages,the sexes and particulalry subpopulations range differently on the r/K continuum! Literature: Reznick et al. 2002, Ecology 83. rK

10. The growth of populations Time Number of deaths Equilibrium Number of births Population size The net reproductive rate R is the number of reproducing female offspring produced per female per generation. Birth excess If R > 1: population size increases If R = 1: population remains stable If R < 1: population size decreases Population size Time Population fluctuations Amplitude Equilibrium density The density of a population is the average number of individuals per unit of area. Abundance is the total number of individuals in a given habitat.

11. North atlantic gannets in north-western England (Nelson 1978) The exponential growth of populations Population size Time Population doubling time If r > 0: population size increases If r = 0: population remains stable If r < 0: population size decreases The intrinsic rate of population growth r (per-capita growth rate) is fraction of population change per unit of time. Under exponential growth there is no equilibrium density. Exponential growth is not a realistic model since populations cannot infinite sizes. The growth rate is r = 0.057

12. The logistic growth of populations Populations do not increase to infinity. There is an upper boundary, the carrying capacity K. The logistic model of population growth The logistic growth function is the standard model in population ecology Raymond Pearl (1879-1940) Pierre Francois Verhulst (1804-1849)

13. The equilibrium population size Maximum population growth Time t 0 of maximum growth The logistic growth of populations

14. Growth of yeast cells (data from Carlson 1913) K = 665 K/2 t0t0 t 0 =7.70 How to estimate the population parameters? Logistic growth occurs particularly in organisms with non-overlapping (discrete) populations, particularly in semelparous species: e.g. bacteria, protists, single celled fungi, insects.

15. Logistic population growth implies a density dependent regulation of population size If N > K, dN/dt < 0: the population decreases Density dependence means that the increase or decrease in population size is regulated by population size. The mechanism of regulation is intraspecific competition. The number of offspring decrease with increasing population size due to resource shortage. Natural variability in population size

16. The Allee effect Logistic growth is equivalent to a quadratic function of population growth Population growth No Allee effect Weak Allee effectStrong Allee effect NN N KK KK/2 At low population size propolation growth is in many cases lower than predicted by the logistic growth equation. Allee extension of the logistic function A is an empitical factor that determines the strength of the Allee effect Most often Allee effects are caused by mate limitation at low population densities

17. Variability in population size Proportional rescaling Poisson randomDensity regulated We use the variance mean ratio as a measure of the type of density fluctuation The Lloyd index of aggregation needs  > > 1. J=1.14 J=0.91J=0.82 Proportional rescaling Taylor’s power law Aphids Butterflies Birds

18. Fragmented landscapes Landscape ecology Agroecology

19. The metapopulation of Melitaea cinxia Glanville fritillary Melitaea cinxia Illka Hanski In fragmented landscapes populations are dived into small local populations separated by an inhostile matrix. Between the habitat patches migration occurs. Such a fragmented population structure connected by dispersal is called a metapopulation.

20. Different types of metapopulations

21. The Lotka – Volterra model of population growth Levins (1969) assumed that the change in the occupancy of single spatially separated habitats (islands) follows the same model. Assume P being the number of islands (total K) occupied. Q= K-P is then the proportion of not occupied islands. m is the immigration and e the local extinction probability. Colonisations Emigration/Extinction The Levins model of meta-populations Dispersal in a fragmented landscape

22. 80 200 90 100 150 Fragments differ in population size The higher the population size is, the lower is the local extinction probability and the higher is the emigration rate Colonisation probability is exponentially dependent on the distance of the islands and extinction probability scales proportionally to island size. The canonical model of metapopulation ecology Distance Metapopulation modelling allows for an estimation of species survival in fragmented landscapes and provides estimates on species occurrences. If we deal with the fraction of fragments colonized

23. Extinction times When is a metapopulation stable? The meta-population is only stable if m > e. If we know local extinction times T L we can estimate the regional time T R to extinction 0 200 400 600 800 1000 1200 01234567 p K 0.5 Median time to extinction The condition for long-term survival

24. What does metapopulation ecology predict? Occurrences of Hesperia comma in fragmented landscapes in southern England (from Hanski 1994) Occurrences Absences In fragmented landscapes occupancy declines nonlinear with decreasing patch area and with decreasing conncetivity (increasing isolation) Predicted extinction threshold

25. Local time to extinction 15102050 SpeciesOccurrencesRegional time to extinction Pterostichus oblongopunctatus1410975483109662193354832 Pseudoophonus rufipes13261292585161290 Pterostichus nigrita13261292585161290 Patrobus atrorufus1273774148369 Platynus assimilis114204079198 Carabus nemoralis114204079198 Harpalus 4-punctatus103142754136 Amara brunea92112142106 Badister bullatus829183589 Oodes gracilis728153177 Loricera pilicornis617142870 Amara communis617142870 Notiophilus biguttatus516132664 Badister sodalis416122460 Carabus hortensis316112357 Harpalus solitaris215112254 Lasiotrechus discus215112254 Amara aulica115102152 Extinction times of ground beetles on 15 Mazurian lake islands Local extinction times (generations) are roughly proportional to local abundances Population should be save if they occupy at least 12 islands.

26. SPOMSIM Population ecology needs long-term data sets